Lupus nephritis (LN) results in renal failure in up to 42% of patients after five years. However, traditional biomarkers and clinical indicators of treatment response often cannot detect treatment failure until irreversible damage to the kidneys has occurred. Therefore, a more reliable means of determining diagnosis and response to induction therapy in LN is needed. The goals of this project are to provide clinicians and investigators with 1) a non-invasive, reliable tool for diagnosis of International Society of Nephrology/Renal Pathology Society (ISN/RPS) class (Class) in LN, 2) a short-term marker of long-term response to therapy, 3) validated assays of biomarkers for clinical use, and 4) hypotheses about mechanisms of response to therapy that can be used as future therapeutic targets. During the first three years of funding, biomarker discovery efforts provided preliminary data that has focused efforts on a limited number of candidate markers. Markers of reactive intermediate production also showed promise as biomarkers. Links between candidate biomarkers and oxidative stress in LN were suggested by our preliminary data and pathways analysis. In preliminary studies, models using this narrowed set of markers had increased predictive power in diagnosing Class and predicting response to therapy over traditional biomarker or single biomarkers models. We hypothesize that changes in expression of and reactive intermediate-induced post-translational modifications of urinary proteins will serve as biomarkers of Class and response to therapy in LN. To address these hypotheses, the following Specific Aims are proposed: 1) Create and validate models of urine biomarkers diagnostic of International Society of Nephrology/Renal Pathology Society (ISN/RPS) biopsy class (Class) in lupus nephritis (LN), 2) Create and validate models of urine biomarkers predictive of renal response to induction therapy in LN, and 3) Develop and validate biomarker assays for clinical use. Subjects will derive from the Charleston and Baltimore inception cohorts and the Genentech LUNAR study population. ISN/RPS class of nephritis will be determined at entry, and outcomes at one year after start of induction therapy will be characterized by the American College of Rheumatology and Systemic Lupus Erythematosus International Cooperating Clinics renal response criteria. Urine will be collected at zero and three months after entry. Candidate low abundance proteins (chemokines, growth factors, cytokines, and renal damage markers selected from discovery analysis in the first funding period) will be quantitated by the Luminex bead assay and ELISA. Post-translational modifications of urine protein tyrosines (Tyr) indicative of exposure to reactive intermediates will be detected by high performance liquid chromatography-electrochemical detection (HPLC-EC). Levels of individual markers at baseline (for Aim 1) and baseline and three months (for Aim 2) will be used to create a mathematical models (by artificial neural network (ANN) that use the levels of surrogate urine protein markers to: 1) diagnose Class of LN at baseline and 2) predict response therapy at one year. Due to the technical nature of machine learning, a reviewer familiar with ANN analysis is requested. In the third Aim, an immunoassay for reactive-intermediate-modified Tyr will be developed, and all biomarker immunoassays will undergo method validation suitable for formal biomarker qualification. This latter step is essential to rapid acceptance and application of the findings of this study to the clinic. Pathways analysis will be used to explore mechanistic interactions between biomarkers deemed predictive in the first two aims. Hypotheses regarding mechanisms of disease activity, damage, and response to therapy will be identified that could be exploited with targeted therapies. This study is unique in 1) the size, diversity, and rigorous characterization of the population used to train and validate the predictive models and 2) its use of novel and well described biomarkers representing diverse pathogenic mechanisms to be tested in a single model. The long-term goal is to provide clinicians with a qualified biomarker panel coupled with a web-based predictive tool for more accurate diagnosis and therapeutic monitoring of LN.